Fast Autonomous Crater Detection by Image Analysis-For Unmanned Landing on Unknown Terrain. - Payel Sadhukhan; Sarbani Palit (ICISP, 2016)

Multi-label Learning Through Minimum Spanning Tree-Based Subset Selection and Feature Extraction. - Payel Sadhukhan; C. A. Murthy (Canadian AI, 2017)

Learning Minority Class prior to Minority Oversampling. - Payel Sadhukhan (IJCNN, 2019)

Reverse-nearest neighborhood based oversampling for imbalanced, multi-label datasets. - Payel Sadhukhan; Sarbani Palit (Pattern Recognit. Lett., 2019)

Can Reverse Nearest Neighbors Perceive Unknowns? - Payel Sadhukhan (IEEE Access, 2020)

Lattice and Imbalance Informed Multi-Label Learning. - Payel Sadhukhan; Sarbani Palit (IEEE Access, 2020)

Adaptive learning of minority class prior to minority oversampling. - Payel Sadhukhan; Sarbani Palit (Pattern Recognit. Lett., 2020)

ML-NCA: Multi-label Neighbourhood Component Analysis. - Arjun Pakrashi; Payel Sadhukhan; Brian Mac Namee (LIDTA@ECML/PKDD, 2021)

Multi-label learning on principles of reverse k-nearest neighbourhood. - Payel Sadhukhan; Sarbani Palit (Expert Syst. J. Knowl. Eng., 2021)

Exploring the Pertinence of Distance Functions for Nominal Multi-label Data. - Payel Sadhukhan (AIAI (2), 2022)

Be Informed of the Known to Catch the Unknown. - Payel Sadhukhan; Sarbani Palit (PRICAI (1), 2022)

Random Walk-steered Majority Undersampling. - Payel Sadhukhan; Arjun Pakrashi; Brian Mac Namee (SMC, 2022)

Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label Data. - Payel Sadhukhan; Arjun Pakrashi; Sarbani Palit; Brian Mac Namee (ICAART (2), 2023)

Oversampling the minority class using a dedicated fitness function and genetic algorithmic progression. - Payel Sadhukhan; Sarbani Palt (Concurr. Comput. Pract. Exp., 2023)

Deploying model obfuscation: towards the privacy of decision-making models on shared platforms. - Payel Sadhukhan; Tanujit Chakraborty; Kausik Sengupta (AMCIS, 2024)

Knowing the class distinguishing abilities of the features, to build better decision-making models. - Payel Sadhukhan; Kausik Sengupta; Sarbani Palit; Tanujit Chakraborty (AMCIS, 2024)

Parameter-Free Undersampling for Multi-Label Data. - Sarbani Palit; Payel Sadhukhan (ICAART (2), 2024)

Explainable AI Using the Wasserstein Distance. - Shion Samadder Chaudhury; Payel Sadhukhan; Kausik Sengupta (IEEE Access, 2024)

Footprints of Data in a Classifier Model: The Privacy Issues and Their Mitigation through Data Obfuscation. - Payel Sadhukhan; Tanujit Chakraborty (CoRR, 2024)

Approximate DBSCAN on obfuscated data. - Payel Sadhukhan; Labani Halder; Sarbani Palit (J. Inf. Secur. Appl., 2024)

Natural-neighborhood based, label-specific undersampling for imbalanced, multi-label data. - Payel Sadhukhan; Sarbani Palit (Adv. Data Anal. Classif., 2024)

Shape Penalized Decision Forests for Imbalanced Data Classification. - Rahul Goswami; Aindrila Garai; Payel Sadhukhan; Palash Ghosh; Tanujit Chakraborty (IEEE Access, 2025)

A framework to undersample and refine the synthetic minority set. - Payel Sadhukhan (Appl. Soft Comput., 2025)

A graph theoretic approach to assess quality of data for classification task. - Payel Sadhukhan; Samrat Gupta (Data Knowl. Eng., 2025)